Low Rank Approximation at Sublinear Cost by Means of Subspace Sampling.
Victor Y. PanQi LuanJohn SvadlenkaLiang ZhaoPublished in: CoRR (2019)
Keyphrases
- low rank approximation
- subspace learning
- singular value decomposition
- low rank matrix approximation
- spectral clustering
- low rank
- eigendecomposition
- kernel matrix
- principal component analysis
- dimensionality reduction
- high dimensional
- face recognition
- data representation
- latent semantic indexing
- high dimensional data
- sparse representation
- least squares
- data dependent
- iterative algorithms
- covariance matrix
- bit rate
- image classification
- semi supervised
- reconstruction error
- pairwise
- feature space
- training data
- similarity measure
- data sets